面向真实世界中疲劳肌肉群识别的肌肉骨骼仿真方法
Towards Real-World Identification of Fatigued Muscle Groups via Musculoskeletal Simulation
AI总结：
提出一种通过比较真实自由空间运动与仿真肌肉骨骼模型来无接触识别上肢疲劳肌肉群的算法，实验证明能可靠区分多个疲劳肌肉群，并展示了如何配置先进仿真器以缩小仿真到现实的差距。
摘要：
无接触诊断肌肉骨骼疾病有望改善人口健康以及协作环境中的机器人行为。然而，当前的诊断方法需要现场体检，由训练有素的医生通过接触感知各肌肉施加的力。虽然存在仿真工具，但将其用于真实数据诊断的研究尚不充分。本文提出一种识别上肢哪个肌肉群疲劳的算法。该算法将受试者的真实自由空间运动与仿真的肌肉骨骼模型运动进行比较，因此是无接触的：避免了侵入式传感或现场评估的需要。我们的算法使用基于物理的肌肉骨骼模型模拟各种疲劳条件，并从真实和仿真数据中提取诊断运动特征，进行比较以进行诊断。在真实数据上的实验结果表明，所提方法能够可靠地区分多个疲劳肌肉群。此外，通过全面的性能比较，我们展示了如何正确配置最新的先进肌肉骨骼仿真器，以解决疲劳诊断任务中的仿真到现实差距。我们的方法有望推动远程和自动化诊断的进一步研究，显著降低大规模早期检测的门槛。
英文摘要：
Contactless diagnosis of musculoskeletal disorders can potentially improve population health as well as robot behaviours in collaborative settings. However, current diagnosis methods require an in-person physical examination in which a trained physician senses, through contact, the force applied by various muscles. Simulation tools exist, but their use for diagnosis with real data is under-explored. In this paper, we propose an algorithm for identifying which upper-limb muscle group is fatigued. Our algorithm compares the realworld free-space motion of the subject with that of a simulated musculoskeletal model, and is therefore contactless: preventing the need for invasive sensing or in-person assessment. Our algorithm simulates various fatigue conditions using a physics-based musculoskeletal model and extracts diagnostic motion features from both real and simulated data, which are compared for diagnosis. Experimental results on real data demonstrate that the proposed method can reliably distinguish between multiple muscle-groups of fatigue. Additionally, through comprehensive performance comparisons, we show how recent advanced musculoskeletal simulators can be properly configured to address the sim-to-real gap in the context of the fatigue diagnosis task. Our approach can potentially spur further research in remote and automated diagnosis, significantly lowering the barrier to large-scale and early detection.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
